AI- boosted and motion- corrected, wireless near- infrared sensing system for continuously monitoring laryngeal muscles

• 成果类型: Article
• 署名作者: Liu, Yihan; Putcha, Arjun; Lyda, Gavin; Peng, Nanqi; Pai, Salil; Nguyen, Tien; Xing, Sicheng; Peng, Shang; Fan, Yiyang; Wu, Yizhang; Xie, Wanrong; Bai, Wubin
• 署名单位: University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill; North Carolina State University; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-9029
• DOI: 10.1073/pnas.2410750121
• 发表日期: 2024-12-13
• 关键词: sensor; CLASSIFICATION; dysphagia

摘要

Neuromuscular diseases pose significant health and economic challenges, necessitating innovative monitoring technologies for personalizable treatment. Existing devices detect muscular motions either indirectly from mechanoacoustic signatures on skin surface or via ultrasound waves that demand specialized skin adhesion. Here, we report a wireless wearable system, Laryngeal Health Monitor (LaHMo), designed to be conformally placed on the neck for continuously measuring movements of underlying muscles. The system uses near- infrared (NIR) light that features deep- tissue penetration and strong interaction with myoglobin to capture muscular locomotion. The incorporated inertial measurement unit sensor further decouples the superposition of signals from NIR recordings. Integrating a multimodal AI- boosted algorithm based on recurrent neural network, the system accurately classifies activities of physiological events. An adaptive model enables fast individualization without enormous data sources from the target user, facilitating its broad applicability. Long- term tests and simulations suggest the potential efficacy of the LaHMo platform for real- world applications, such as monitoring disease progression in neuromuscular disorders, evaluating treatment efficacy, and providing biofeedback for rehabilitation exercises. The LaHMo platform may serve as a general noninvasive, user- friendly solution for assessing neuromuscular function beyond the anterior neck, potentially improving diagnostics and treatment of various neuromuscular disorders.